SoroushVahidi/scidocs huggingface dataset:
Extension of Soft-Switching Region of Dual-Active-Bridge Converter by a Tunable Resonant Tank
(2017)

Hard-switching-induced switching loss can contribute significantly to the power loss of an isolated bidirectional dual-active-bridge (DAB) dc–dc converter operating at high frequency. An <inline-formula> <tex-math notation="LaTeX">$LC$</tex-math></inline-formula>-type series resonant DAB converter based on a switch-controlled-inductor (SCI) is proposed to mitigate the loss arising from hard switching under wide-range variations in output voltage and current. Zero-voltage switching is achieved at the primary side (high voltage), while at the secondary side (low voltage), zero-current switching is preferred to reduce excessive ringing due to circulating current and switching loss. In order to achieve reduced conduction loss, a nominal operating point is chosen where the root-mean-square resonant tank current is the minimum. To validate the proposed topology and modulation scheme, an <inline-formula><tex-math notation="LaTeX">$LC$</tex-math></inline-formula>-type series resonant DAB converter based on SCI operating at 100 kHz is designed to interface a 400-V dc bus to a supercapacitor-based energy storage. Simulation and experimental results validate the effectiveness of the proposed topology for charging/discharging a supercapacitor with an output voltage variation of between 10 and 48 V and a maximum rated power of 480 W. A maximum efficiency of 94.6% is achieved using the proposed topology and modulation scheme.
WordNet Atlas : a web application for visualizing WordNet as a zoomable map
(2011)

The English WordNet is a lexical database containing more than 206,900 wordconcept pairs and more than 377,500 semantic and lexical links. Trying to visualize them all at once as a node-link diagram results in a representation which can be too big and complex for the user to grasp, and which cannot be easily processed by current web technologies. We propose a visualization technique based on the concept of semantic zooming, usually found in popular web map applications. This technique makes it feasible to handle large and complex graphs, and display them in an interactive representation. By zooming, it is possible to switch from an overview visualization, quickly and clearly presenting the global characteristics of the structure, to a detailed one, displaying a classic node-link diagram. WordNet Atlas is a web application that leverages this method to aid the user in the exploration of the WordNet data set, from its global taxonomy structure to the detail of words and synonym sets.
Reduced-Order Model and Stability Analysis of Low-Voltage DC Microgrid
(2013)

Depleting fossil fuels, increasing energy demand, and need for high-reliability power supply motivate the use of dc microgrids. This paper analyzes the stability of low-voltage dc microgrid systems. Sources are controlled using a droop-based decentralized controller. Various components of the system have been modeled. A linearized system model is derived using small-signal approximation. The stability of the system is analyzed by identifying the eigenvalues of the system matrix. The sufficiency condition for stable operation of the system is derived. It provides upper bound on droop constants and is useful during planning and designing of dc microgrids. Furthermore, the sensitivity of system poles to variation in cable resistance and inductance is identified. It is proved that the poles move further inside the negative real plane with a decrease in inductance or an increase in resistance. The method proposed in this paper is applicable to any interconnecting structure of sources and loads. The results obtained by analysis are verified by detailed simulation study. Root locus plots are included to confirm the movement of system poles. The viability of the model is confirmed by experimental results from a scaled-down laboratory prototype of a dc microgrid developed for the purpose.
Hierarchical Control of Droop-Controlled AC and DC Microgrids—A General Approach Toward Standardization
(2011)

AC and dc microgrids (MGs) are key elements for integrating renewable and distributed energy resources as well as distributed energy-storage systems. In the last several years, efforts toward the standardization of these MGs have been made. In this sense, this paper presents the hierarchical control derived from ISA-95 and electrical dispatching standards to endow smartness and flexibility to MGs. The hierarchical control proposed consists of three levels: 1) The primary control is based on the droop method, including an output-impedance virtual loop; 2) the secondary control allows the restoration of the deviations produced by the primary control; and 3) the tertiary control manages the power flow between the MG and the external electrical distribution system. Results from a hierarchical-controlled MG are provided to show the feasibility of the proposed approach.
Secondary Load-Frequency Control for MicroGrids in Islanded Operation
(2005)

The objective of this paper is to present novel control strategies for MicroGrid operation, especially in islanded mode. The control strategies involve mainly the coordination of secondary load-frequency control by a MicroGrid Central Controller that heads a hierarchical control system able to assure stable and secure operation when the islanding of the MicroGrid occurs and in load-following situations in islanded mode.
Decentralized Control for Parallel Operation of Distributed Generation Inverters Using Resistive Output Impedance
(2005)

In this paper, a novel wireless load-sharing controller for islanding parallel inverters in an ac-distributed system is proposed. This paper explores the resistive output impedance of the parallel-connected inverters in an island microgrid. The control loops are devised and analyzed, taking into account the special nature of a low-voltage microgrid, in which the line impedance is mainly resistive and the distance between the inverters makes the control intercommunication between them difficult. In contrast with the conventional droop-control method, the proposed controller uses resistive output impedance, and as a result, a different control law is obtained. The controller is implemented by using a digital signal processor board, which only uses local measurements of the unit, thus increasing the modularity, reliability, and flexibility of the distributed system. Experimental results are provided from two 6-kVA inverters connected in parallel, showing the features of the proposed wireless control
Eval all, trust a few, do wrong to none: Comparing sentence generation models
(2018)

In this paper, we study recent neural generative models for text generation related to variational autoencoders. These models employ various techniques to match the posterior and prior distributions, which is important to ensure a high sample quality and a low reconstruction error. In our study, we follow a rigorous evaluation protocol using a large set of previously used and novel automatic metrics and human evaluation of both generated samples and reconstructions. We hope that it will become the new evaluation standard when comparing neural generative models for text.
DEEP LEARNING AND INTELLIGENT AUDIO MIXING
(2017)

Mixing multitrack audio is a crucial part of music production. With recent advances in machine learning techniques such as deep learning, it is of great importance to conduct research on the applications of these methods in the field of automatic mixing. In this paper, we present a survey of intelligent audio mixing systems and their recent incorporation of deep neural networks. We propose to the community a research trajectory in the field of deep learning applied to intelligent music production systems. We conclude with a proof of concept based on stem audio mixing as a contentbased transformation using a deep autoencoder.
Coordination and Geometric Optimization via Distributed Dynamical Systems
(2005)

Emerging applications for networked and cooperative robots motivate the study of motion coordination for groups of agents. For example, it is envisioned that groups of agents will perform a variety of useful tasks including surveillance, exploration, and environmental monitoring. This paper deals with basic interactions among mobile agents such as “move away from the closest other agent” or “move toward the furthest vertex of your own Voronoi polygon.” These simple interactions amount to distributed dynamical systems because their implementation requires only minimal information about neighboring agents. We characterize the close relationship between these distributed dynamical systems and the disk-covering and sphere-packing cost functions from geometric optimization. Our main results are: (i) we characterize the smoothness properties of these geometric cost functions, (ii) we show that the interaction laws are variations of the nonsmooth gradient of the cost functions, and (iii) we establish various asymptotic convergence properties of the laws. The technical approach relies on concepts from computational geometry, nonsmooth analysis, and nonsmooth stability theory.
Online fuzzy c means
(2008)

Clustering streaming data presents the problem of not having all the data available at one time. Further, the total size of the data may be larger than will fit in the available memory of a typical computer. If the data is very large, it is a challenge to apply fuzzy clustering algorithms to get a partition in a timely manner. In this paper, we present an online fuzzy clustering algorithm which can be used to cluster streaming data, as well as very large data sets which might be treated as streaming data. Results on several large volumes of magnetic resonance images show that the new algorithm produces partitions which are very close to what you could get if you clustered all the data at one time. So, the algorithm is an accurate approach for online clustering.
The architecture of subterranean ant nests: beauty and mystery underfoot
(2015)

Over the 100 million years of their evolution, ants have constructed or occupied nests in a wide range of materials and situations. A large number of ant species excavate nests in the soil, and these subterranean nests have evolved into a wide range of sizes and architectures. On the basis of casts made of such nests, this variation and the patterns that govern it are described. The possible functions of architectural features are discussed, as are the behavioral “rules” through which the nests are created by worker ants.
A Reorder Buffer Design for High Performance Processors
(2012)

Modern reorder buffers (ROBs) were conceived to improve processor performance by allowing instruction execution out of the original program order and run ahead of sequential instruction code exploiting existing instruction level parallelism (ILP). The ROB is a functional structure of a processor execution engine that supports speculative execution, physical register recycling, and precise exception recovering. Traditionally, the ROB is considered as a monolithic circular buffer with incoming instructions at the tail pointer after the decoding stage and completing instructions at the head pointer after the commitment stage. The latter stage verifies instructions that have been dispatched, issued, executed, and are not completed speculatively. This paper presents a design of distributed reorder buffer microarchitecture by using small structures near building blocks which work together, using the same tail and head pointer values on all structures for synchronization. The reduction of area, and therefore, the reduction of power and delay make this design suitable for both embedded and high performance microprocessors.
Achieving ICS Resilience and Security through Granular Data Flow Management
(2016)

Modern Industrial Control Systems (ICS) rely on enterprise to plant floor connectivity. Where the size, diversity, and therefore complexity of ICS increase, operational requirements, goals, and challenges defined by users across various sub-systems follow. Recent trends in Information Technology (IT) and Operational Technology (OT) convergence may cause operators to lose a comprehensive understanding of end-to-end data flow requirements. This presents a risk to system security and resilience. Sensors were once solely applied for operational process use, but now act as inputs supporting a diverse set of organisational requirements. If these are not fully understood, incomplete risk assessment, and inappropriate implementation of security controls could occur. In search of a solution, operators may turn to standards and guidelines. This paper reviews popular standards and guidelines, prior to the presentation of a case study and conceptual tool, highlighting the importance of data flows, critical data processing points, and system-to-user relationships. The proposed approach forms a basis for risk assessment and security control implementation, aiding the evolution of ICS security and resilience.
Adversarial Frontier Stitching for Remote Neural Network Watermarking
(2017)

The state of the art performance of deep learning models comes at a high cost for companies and institutions, due to the tedious data collection and the heavy processing requirements. Recently, Uchida et al. (2017) proposed to watermark convolutional neural networks by embedding information into their weights. While this is a clear progress towards model protection, this technique solely allows for extracting the watermark from a network that one accesses locally and entirely. This is a clear impediment, as leaked models can be re-used privately, and thus not released publicly for ownership inspection. Instead, we aim at allowing the extraction of the watermark from a neural network (or any other machine learning model) that is operated remotely, and available through a service API. To this end, we propose to operate on the model’s action itself, tweaking slightly its decision frontiers so that a set of specific queries convey the desired information. In present paper, we formally introduce the problem and propose a novel zerobit watermarking algorithm that makes use of adversarial model examples (called adversaries for short). While limiting the loss of performance of the protected model, this algorithm allows subsequent extraction of the watermark using only few remote queries. We experiment this approach on the MNIST dataset with three types of neural networks, demonstrating that e.g., watermarking with 100 images incurs a slight accuracy degradation, while being resilient to most removal attacks.
Strokelets: A Learned Multi-scale Representation for Scene Text Recognition
(2014)

Driven by the wide range of applications, scene text detection and recognition have become active research topics in computer vision. Though extensively studied, localizing and reading text in uncontrolled environments remain extremely challenging, due to various interference factors. In this paper, we propose a novel multi-scale representation for scene text recognition. This representation consists of a set of detectable primitives, termed as strokelets, which capture the essential substructures of characters at different granularities. Strokelets possess four distinctive advantages: (1) Usability: automatically learned from bounding box labels, (2) Robustness: insensitive to interference factors, (3) Generality: applicable to variant languages, and (4) Expressivity: effective at describing characters. Extensive experiments on standard benchmarks verify the advantages of strokelets and demonstrate the effectiveness of the proposed algorithm for text recognition.
Unsupervised Footwear Impression Analysis and Retrieval from Crime Scene Data
(2014)

Footwear impressions are one of the most frequently secured types of evidence at crime scenes. For the investigation of crime series they are among the major investigative notes. In this paper, we introduce an unsupervised footwear retrieval algorithm that is able to cope with unconstrained noise conditions and is invariant to rigid transformations. A main challenge for the automated impression analysis is the separation of the actual shoe sole information from the structured background noise. We approach this issue by the analysis of periodic patterns. Given unconstrained noise conditions, the redundancy within periodic patterns makes them the most reliable information source in the image. In this work, we present four main contributions: First, we robustly measure local periodicity by fitting a periodic pattern model to the image. Second, based on the model, we normalize the orientation of the image and compute the window size for a local Fourier transformation. In this way, we avoid distortions of the frequency spectrum through other structures or boundary artefacts. Third, we segment the pattern through robust point-wise classification, making use of the property that the amplitudes of the frequency spectrum are constant for each position in a periodic pattern. Finally, the similarity between footwear impressions is measured by comparing the Fourier representations of the periodic patterns. We demonstrate robustness against severe noise distortions as well as rigid transformations on a database with real crime scene impressions. Moreover, we make our database available to the public, thus enabling standardized benchmarking for the first time.
An approach to online identification of Takagi-Sugeno fuzzy models
(2004)

An approach to the online learning of Takagi-Sugeno (TS) type models is proposed in the paper. It is based on a novel learning algorithm that recursively updates TS model structure and parameters by combining supervised and unsupervised learning. The rule-base and parameters of the TS model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. In this way, the rule-base structure is inherited and up-dated when new data become available. By applying this learning concept to the TS model we arrive at a new type adaptive model called the Evolving Takagi-Sugeno model (ETS). The adaptive nature of these evolving TS models in combination with the highly transparent and compact form of fuzzy rules makes them a promising candidate for online modeling and control of complex processes, competitive to neural networks. The approach has been tested on data from an air-conditioning installation serving a real building. The results illustrate the viability and efficiency of the approach. The proposed concept, however, has significantly wider implications in a number of fields, including adaptive nonlinear control, fault detection and diagnostics, performance analysis, forecasting, knowledge extraction, robotics, behavior modeling.
Automatic speech recognition and speech variability: A review
(2007)

Major progress is being recorded regularly on both the technology and exploitation of automatic speech recognition (ASR) and spoken language systems. However, there are still technological barriers to flexible solutions and user satisfaction under some circumstances. This is related to several factors, such as the sensitivity to the environment (background noise), or the weak representation of grammatical and semantic knowledge. Current research is also emphasizing deficiencies in dealing with variation naturally present in speech. For instance, the lack of robustness to foreign accents precludes the use by specific populations. Also, some applications, like directory assistance, particularly stress the core recognition technology due to the very high active vocabulary (application perplexity). There are actually many factors affecting the speech realization: regional, sociolinguistic, or related to the environment or the speaker herself. These create a wide range of variations that may not be modeled correctly (speaker, gender, speaking rate, vocal effort, regional accent, speaking style, non-stationarity, etc.), especially when resources for system training are scarce. This paper outlines current advances related to these topics. 2007 Elsevier B.V. All rights reserved.
Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks
(2017)

Early detection of pulmonary cancer is the most promising way to enhance a patient’s chance for survival. Accurate pulmonary nodule detection in computed tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In this paper, inspired by the successful use of deep convolutional neural networks (DCNNs) in natural image recognition, we propose a novel pulmonary nodule detection approach based on DCNNs. We first introduce a deconvolutional structure to Faster Region-based Convolutional Neural Network (Faster R-CNN) for candidate detection on axial slices. Then, a three-dimensional DCNN is presented for the subsequent false positive reduction. Experimental results of the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior detection performance of the proposed approach on nodule detection(average FROC-score of 0.891, ranking the 1st place over all submitted results).
Kalman Particle Filter for lane recognition on rural roads
(2009)

Despite the availability of lane departure and lane keeping systems for highway assistance, unmarked and winding rural roads still pose challenges to lane recognition systems. To detect an upcoming curve as soon as possible, the viewing range of image-based lane recognition systems has to be extended. This is done by evaluating 3D information obtained from stereo vision or imaging radar in this paper. Both sensors deliver evidence grids as the basis for road course estimation. Besides known Kalman Filter approaches, Particle Filters have recently gained interest since they offer the possibility to employ cues of a road, which can not be described as measurements needed for a Kalman Filter approach. We propose to combine both principles and their benefits in a Kalman Particle Filter. The comparison between the results gained from this recently published filter scheme and the classical approaches using real world data proves the advantages of the Kalman Particle Filter.
A risk taxonomy proposal for software maintenance
(2005)

There can be no doubt that risk management is an important activity in the software engineering area. One proof of this is the large body of work existing in this area. However, when one takes a closer look at it, one perceives that almost all this work is concerned with risk management for software development projects. The literature on risk management for software maintenance is much scarcer. On the other hand, software maintenance projects do present specificities that imply they offer different risks than development. This suggests that maintenance projects could greatly benefit from better risk management tools. One step in this direction would be to help identifying potential risk factors at the beginning of a maintenance project. For this, we propose taxonomy of possible risks for software management projects. The ontology was created from: i) an extensive survey of risk management literature, to list known risk factors for software development; and, ii) an extensive survey of maintenance literature, to list known problems that may occur during maintenance.